{"ID":2855252,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.13619","arxiv_id":"2510.13619","title":"Characterizing Lidar Point-Cloud Adversities Using a Vector Field Visualization","abstract":"In this paper we introduce a visualization methodology to aid a human analyst in classifying adversity modes that impact lidar scan matching. Our methodology is intended for offline rather than real-time analysis. The method generates a vector-field plot that characterizes local discrepancies between a pair of registered point clouds. The vector field plot reveals patterns that would be difficult for the analyst to extract from raw point-cloud data. After introducing our methodology, we apply the process to two proof-of-concept examples: one a simulation study and the other a field experiment. For both data sets, a human analyst was able to reason about a series of adversity mechanisms and iteratively remove those mechanisms from the raw data, to help focus attention on progressively smaller discrepancies.","short_abstract":"In this paper we introduce a visualization methodology to aid a human analyst in classifying adversity modes that impact lidar scan matching. Our methodology is intended for offline rather than real-time analysis. The method generates a vector-field plot that characterizes local discrepancies between a pair of register...","url_abs":"https://arxiv.org/abs/2510.13619","url_pdf":"https://arxiv.org/pdf/2510.13619v1","authors":"[\"Daniel Choate\",\"Jason Rife\"]","published":"2025-10-15T14:49:27Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}